Action optimization device, method and program

ABSTRACT

Provided is a highly reliable technology for optimizing an action for controlling an environment in a target space. An action optimization device for optimizing an action for controlling an environment: acquires environmental data related to a state of the environment; performs time/space interpolation on the acquired environmental data; trains an environment reproduction model, based on the time/space-interpolated environmental data, such that, when a state of an environment and an action for controlling the environment are input, a correct answer value for an environmental state after the action is output; trains an exploration model such that an action to be taken next is output when an environmental state output from the environment reproduction model is input; predicts a second environmental state corresponding to a first environmental state and a first action by using the trained environment reproduction model; explores for a second action to be taken for the second environmental state; and outputs a result of the exploration.

CROSS-REFERENCE TO RELATED APPLICATIONS

This is a U.S. national phase application under 35 USC 371 ofinternational application PCT/JP2019/027911, filed on Jul. 16, 2019,which claims priority to Japanese patent application No. 2018-141754,filed on Jul. 27, 2018. The entire disclosures of the above applicationsare incorporated herein by reference.

FIELD

An aspect of this invention relates generally to an action optimizationdevice, a method, and a program for optimizing an action for controllingan environment in a target space.

BACKGROUND

In management of buildings and facilities such as office buildings, atechnique has been proposed for optimizing operations (hereinafter,collectively referred to as “control” or “action for controlling anenvironment”) to be performed with respect to an operation state ofdevices and equipment including air conditioners, and arrangement ofcleaning personnel. For example, a building energy management system(BEMS) (see Non-Patent Literature 1) for the purpose of grasping andreducing energy consumption of air conditioners, etc., and a cleaningoptimization system (see Non-Patent Literature 2) for optimizing thenumber of cleaning staff in accordance with the number of users of atoilet are known. In these techniques, various feedback-typeoptimization systems utilizing various data are used.

On the other hand, a feedforward-type optimization system is known whichmeasures a flow and the number of people in a predetermined space(hereinafter, referred to as a “people flow”) and controls an operationof a device taking into account a predicted value based on themeasurement result (see Patent Document 1). In the technique of PatentDocument 1, a people flow ratio of an adjacent space known to have acorrelation with a target space is measured in advance, a predictedamount of people flow of the target space is calculated by multiplyingthe people flow ratio by an amount of people flow obtained from theadjacent space, and an upper limit of an energy consumption amount isset according to the predicted amount of people flow.

CITATION LIST Patent Literature

-   [Patent Document 1] Jpn. Pat. Appln. KOKAI Publication No.    2011-231946

Non Patent Literatures

-   [Non-Patent Literature 1] Tomohiro Asazuma, “Smart BEMS that    balances building comfort and energy saving and supports safety and    security,” Toshiba Review, Vol. 68, No. 12 (2013), pp. 26-29-   [Non-Patent Literature 2] Toru Nabeyama, “Research on New Business    Possibilities and Areas of IoT”, Nikkei Research Monthly    Report, 2017. 5, pp. 74-83

SUMMARY Technical Problem

However, since a feedback-type system is used in the techniquesdescribed in Non-Patent Literature 1 and Non-Patent Literature 2, where,for example, a non-optimal state in which a room temperature in afacility becomes too cold or dirt becomes conspicuous is detected andcontrol is optimized. Thus, a time lag until returning to an optimalstate is a problem.

On the other hand, in the technique described in Patent Document 1, afeedforward-type optimization system that takes into account a predictedvalue of a people flow, which is one of the factors causing anon-optimal state, is used. However, the system of Patent Document 1simply follows a short-term increase and decrease of the people flow,and thus cannot optimize control taking into account a medium-term andlong-term increase and decrease of the people flow such as whether thepeople flow is continuously large or the people flow rapidly decreases.In addition, since an upper limit of an energy consumption amount issimply adjusted without estimating an effect of a control change, thesystem cannot consider control following an interaction existing in atarget space such as impaired comfort of a user due to heat accumulationcaused by people crowded in a place away from a representative point orimpaired energy saving due to a prediction error caused by inflow andoutflow of cold and hot air from and to the vicinity. Further, it is noteasy to use the system for optimization problems other than the airconditioning control.

The present invention has been made in view of the above circumstances,and an object of the present invention is to provide a highly reliableaction optimization technique for optimizing an action for controllingan environment in a target space taking into account a predicted effect.

Solution to Problem

In order to solve the above problem, a first aspect of the presentinvention provides an action optimization device for optimizing anaction for controlling an environment in a target space, comprising anenvironmental data acquisition part that acquires environmental datarelated to a state of the environment in the target space; anenvironmental data interpolation part that performs time/spaceinterpolation on the acquired environmental data according to a presetalgorithm; an environment reproduction model training part that trainsan environment reproduction model, based on the time/space-interpolatedenvironmental data, such that, when a state of an environment and anaction for controlling the environment are input, a correct answer valuefor an environmental state after the action is output; an explorationmodel training part that trains an exploration model such that an actionto be taken next is output when an environmental state output from theenvironment reproduction model is input; an environment reproductionpart that predicts a second environmental state corresponding to a firstenvironmental state and a first action by using the environmentreproduction model; an exploration part that explores for a secondaction to be taken for the second environmental state by using theexploration model; and an output part that outputs a result of theexploration by the exploration part.

According to a second aspect of the present invention, in the abovefirst aspect, the exploration part outputs the explored for secondaction to the environment reproduction part, the environmentreproduction part further predicts a third environmental statecorresponding to the second environmental state and the second actionoutput from the exploration part by using the environment reproductionmodel, and the exploration part further explores for a third action tobe taken for the third environmental state by using the explorationmodel.

According to a third aspect of the present invention, in the above firstaspect, the environment reproduction part further outputs a rewardcorresponding to the second environmental state based on a preset rewardfunction, and the exploration model training part updates a trainingresult of the exploration model based on the reward output from theenvironment reproduction part.

According to a fourth aspect of the present invention, in the abovefirst aspect, the device further comprises an environment predictionpart that performs future prediction using a preset time-series analysismethod based on the environmental data to generate environmentprediction data, and the exploration part explores for an action to betaken by using the environment prediction data for the explorationmodel.

According to a fifth aspect of the present invention, in the above firstaspect, the device further comprises an environment expansion part thatperforms data augmentation on the environmental data based on a randomnumber, and the environment reproduction model training part trains theenvironment reproduction model by using the environmental data subjectedto the data augmentation.

According to a sixth aspect of the present invention, in the above firstaspect, the device further comprises a policy data acquisition part thatacquires policy data specifying information to be used for processing bythe environment reproduction model training part, the exploration modeltraining part, the environment reproduction part, or the explorationpart.

According to a seventh aspect of the present invention, in the firstaspect, the exploration part explores for, as the second action, anaction of a group unit for a control target group obtained by grouping aplurality of control targets based on a predetermined criterion inadvance, or a series of actions for one or more control targets forrealizing a predetermined function.

Advantageous Effects of Invention

According to the first aspect of the present invention, an environmentreproduction model is trained with a correspondence relationship amongan environmental state, an action therefor, and an environmental stateafter the action, by using training data including three types of datarespectively corresponding thereto, based on time/space-interpolatedenvironmental data in a target space, and a change in environmentalstate is predicted from an environmental state and an action based onthe trained environment reproduction model. In addition, an explorationmodel is trained such that an action to be taken next is output when anenvironmental state is input, and an action to be taken for theenvironmental state predicted by the environment reproduction model canthus be explored for by using the trained exploration model.

In this way, since time/space interpolation is performed on the acquiredenvironmental data, it is possible to train the environment reproductionmodel by using data in a discretionary period without being limited todata at a specific time point in the past. By using such an environmentreproduction model, it is possible to obtain a more reliable predictionresult and perform a more reliable exploration using the predictionresult. In addition, since environmental data other than arepresentative point can be used, it is possible to also explore for anaction taking into account local environmental conditions such as heataccumulation. Note that heat accumulation generally refers to a localspace having a higher temperature than the surroundings. Also, by usingenvironmental data including a wide variety of information, it ispossible to perform training or an exploration adapted to variousconditions, not limited to air conditioning control, for example.Furthermore, since two types of models are used for the environmentprediction and the exploration, it is also possible to further improvethe reliability of the prediction by individually performingverification and adjustment by the respective devices.

According to the second aspect of the present invention, in the abovefirst aspect, the environmental state predicted using the environmentreproduction model and the action explored for using the explorationmodel for the environmental state are input to the environmentreproduction model again, and a new environmental state is predicted.Then, the new environmental state is input to the exploration modelagain, and a further new action is explored for. Thereby, as theprediction result using the environment reproduction model and theexploration result using the exploration model function in a chainedmanner, it is possible to continue the exploration even if a pair of anenvironmental state and an action that does not exist in the trainingdata is selected, obtaining actions to be taken for environmental statescorresponding to a plurality of time points as a series of explorationresults.

According to the third aspect of the present invention, in the abovefirst aspect, a reward corresponding to a second environmental statepredicted from a first environmental state and a first action thereforis obtained based on a preset reward function, and an update of theexploration model is performed based on the obtained reward. Thus, evenin complicated optimization problems in which training data cannot beuniquely prepared, the exploration model can be trained, and anappropriate action can be explored for and output.

According to the fourth aspect of the present invention, in the abovefirst aspect, an exploration for an action for controlling anenvironment is performed using data predicted by a time-series analysisbased on environmental data. Thereby, even in a situation in which apredicted value related to environment information cannot besufficiently acquired, it is possible to perform an exploration based onhighly reliable prediction data.

According to the fifth aspect of the present invention, in the abovefirst aspect, data augmentation based on a random number is performed onthe acquired environmental data, and training of the environmentreproduction model is performed using the environmental data subjectedto the data augmentation. Since an apparent data amount can be increasedby the data augmentation, it is possible to shorten the time required tocollect a sufficient amount of environmental data for the training ofthe environment reproduction model.

According to the sixth aspect of the present invention, in the abovefirst aspect, policy data related to various pieces of informationnecessary for training of the environment reproduction model, trainingof the exploration model, or evaluation using these models is acquired.This makes it possible to individually set training and evaluationmethods according to a control target, environmental conditions, etc.,to perform more flexible processing.

According to the seventh aspect of the present invention, in the abovefirst aspect, an action of a group unit for a control target group or aseries of actions for realizing a predetermined function is explored foras the second action. This makes it possible to realize further flexibleprocessing according to a control target, a control purpose, a controlenvironment, etc.

That is, according to each aspect of the present invention, it ispossible to provide a highly reliable action optimization technique foroptimizing an action for controlling an environment in a target spacetaking into account predicted effects.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram showing a first example of an overall configurationof a system including an action optimization device according to anembodiment of the present invention.

FIG. 2 is a block diagram showing a hardware configuration of the actionoptimization device shown in FIG. 1 .

FIG. 3 is a block diagram showing a software configuration of the actionoptimization device shown in FIG. 1 .

FIG. 4 is a flowchart showing an example of a processing procedure andprocessing contents of environmental data acquisition by the actionoptimization device shown in FIG. 1 .

FIG. 5A is a diagram showing people flow data as an example of theenvironmental data.

FIG. 5B is a diagram showing temperature data as an example of theenvironmental data.

FIG. 5C is a diagram showing BEMS data as an example of theenvironmental data.

FIG. 6 is a flowchart showing an example of a processing procedure andprocessing contents of prediction of the environmental data by theaction optimization device shown in FIG. 1 .

FIG. 7 is a flowchart showing an example of a processing procedure andprocessing contents of time/space interpolation of the environmentaldata by the action optimization device shown in FIG. 1 .

FIG. 8 is a diagram showing an example of the environmental data to besubjected to the time/space interpolation processing shown in FIG. 7 .

FIG. 9 is a diagram showing a first example of a GUI screen used forspecifying an operation policy of the action optimization device shownin FIG. 1 .

FIG. 10 is a flowchart showing an example of a processing procedure andprocessing contents of policy management by the action optimizationdevice shown in FIG. 1 .

FIG. 11 is a diagram showing an example of policy data includinginformation specifying an operation of the action optimization deviceshown in FIG. 1 .

FIG. 12 is a flowchart showing an example of a procedure and processingcontents of training processing of an environment reproduction model bythe action optimization device shown in FIG. 1 .

FIG. 13 is a flowchart showing an example of a procedure and processingcontents of data augmentation processing by the action optimizationdevice shown in FIG. 1 .

FIG. 14 is a flowchart showing an example of a procedure and processingcontents of evaluation processing using an environment reproductionmodel by the action optimization device shown in FIG. 1 .

FIG. 15 is a diagram showing an example of a GUI screen used forspecifying an exploration operation of the action optimization deviceshown in FIG. 1 .

FIG. 16 is a flowchart showing an example of a procedure and processingcontents of training processing of an exploration model by the actionoptimization device shown in FIG. 1 .

FIG. 17 is a flowchart showing an example of a procedure and processingcontents of evaluation processing using an exploration model by theaction optimization device shown in FIG. 1 .

FIG. 18 is a flowchart showing an example of a processing procedure andprocessing contents of output by an output part of the actionoptimization device shown in FIG. 1 .

FIG. 19 is a diagram showing an example of an exploration result by theaction optimization device shown in FIG. 1 .

FIG. 20 is a diagram showing a second example of an overallconfiguration of a system including an action optimization deviceaccording to an embodiment of the present invention.

FIG. 21 is a diagram showing a second example of a GUI screen used forspecifying an operation policy of the action optimization device shownin FIG. 20 .

DETAILED DESCRIPTION

In the following, embodiments according to the present invention will bedescribed with reference to the accompanying drawings.

Embodiment Example 1

(Configuration)

(1) System

FIG. 1 is a diagram showing a first example of an overall configurationof a system including an action optimization device 1 according to anembodiment of the present invention. In this example, the actionoptimization device 1 is assumed to optimize control of air conditioningas an action for controlling an environment in a target space. Thissystem includes the action optimization device 1, one or more externalsensors Ss1, Ss2, . . . , Ssn or an integration device SI thatintegrates them (hereinafter, collectively referred to as a “sensorsystem SS”) and a building and energy management system MS that existsinside or outside a facility, such as an air conditioning system AS anda cleaning system CS, which are indirectly or directly connected to theaction optimization device 1 via a network NW, a cable, etc.

The network NW includes, for example, an IP (Internet Protocol) networkrepresented by the Internet and a plurality of access networks foraccessing the IP network. As the access networks, not only a wirednetwork using an optical fiber but also, for example, a mobile phonenetwork operating under a standard such as 3G or 4G, a wireless localarea network (LAN), etc. are used.

The external sensors Ss1, Ss2, . . . , Ssn are, for example, sensorsthat acquire various information about an environment in a target space,such as people flow sensors, temperature sensors, humidity sensors, andinfrared sensors, and output various data, such as a people flow, atemperature, a humidity, and the presence/absence of an object. Theintegration device SI can, for example, integrally control operations ofthe external sensors Ss1, Ss2, . . . , Ssn, and integrally collect andtransmit the data output by the external sensors Ss1, Ss2, . . . , Ssn.

The action optimization device 1 according to the embodiment can receivevarious information as environmental data from the sensor system SS andthe building and energy management system MS via the network NW, asignal cable, etc., and transmit a control signal to the building andenergy management system MS.

(2) Action Optimization Device

(2-1) Hardware Configuration

FIG. 2 is a block diagram showing an example of a hardware configurationof the action optimization device 1 according to the embodiment shown inFIG. 1 . The action optimization device 1 includes, for example, apersonal computer or a server device, and includes a hardware processor20A such as a CPU (Central Processing Unit) or an MPU (Micro ProcessingUnit). An input/output interface unit 10, a program memory 20B, and adata memory 30 are connected to the hardware processor 20A via a bus 40.

The input/output interface unit 10 has, for example, a wired or wirelessinterface, and has a function of receiving environmental datatransmitted from the sensor system SS or the building and energymanagement system MS and transmitting a control signal output from theaction optimization device 1 to the building and energy managementsystem MS. The input/output interface unit 10 also enables transmissionand reception of information to and from a display device (not shown)and an input device (not shown). As the wired interface, for example, awired LAN is used, and as the wireless interface, for example, aninterface adopting a low-power wireless data communication standard suchas a wireless LAN or Bluetooth (registered trademark) is used.

The input/output interface unit 10 also includes a GUI (Graphical UserInterface), and can receive a policy instruction or an explorationinstruction input by a user or an operator from the input device (notshown), for example.

The program memory 20B is a combination of a nonvolatile memory that canbe written to and read from at any time, such as a hard disk drive (HDD)or a solid state drive (SSD), and a nonvolatile memory such as a readonly memory (ROM), and stores programs necessary for executing variouscontrolling/processing according to the embodiment.

The data memory 30 is, as a storage medium, a combination of anonvolatile memory that can be written to and read from at any time,such as an HDD or an SSD, and a volatile memory such as a RAM (RandomAccess Memory), and is used to store various data acquired and createdin the course of performing various processing.

(2-2) Software Configuration

FIG. 3 is a block diagram showing a software configuration of the actionoptimization device 1 according to the embodiment shown in FIG. 1 inassociation with the hardware configuration shown in FIG. 2 . The actionoptimization device 1 includes the input/output interface unit 10, acontroller/processor unit 20, and a data memory 30.

A storage area of the data memory 30 includes an environmental datamemory 31, a policy data memory 32, a model data memory 33, and anexploration result memory 34.

The environmental data memory 31 stores environmental data acquired fromthe sensor system SS and the building and energy management system MS.The environmental data is data related to an environment in a targetspace, and may include data representing control over the environment inaddition to data representing a state of the environment. For example,the environmental data may include various kinds of information such asa set temperature and an operation mode of an air conditioner, and acleaning schedule managed by the cleaning system CS, in addition toinformation sensed by various sensors such as a people flow, atemperature, a humidity, the presence of dirt or dust, and an amount ofairborne particles.

The policy data memory 32 stores policy data including instructioninformation on various processing in the action optimization device 1input by the user, etc. via the GUI. The policy data includes, forexample, data used for training and information specifying a trainingmethod.

The model data memory 33 stores model data used by the actionoptimization device 1 for various processing. Each piece of model datais stored in a suitably executable format, such as a binary format, andmay also include metadata representing a model name.

The exploration result memory 34 stores an exploration result obtainedby exploration processing of the action optimization device 1.

However, the above memories 31 to 34 are not essential configurations,and the action optimization device 1 may directly acquire necessary datafrom the sensor system SS or the building and energy management systemMS at any time. Alternatively, the memories 31 to 34 may not beincorporated into the action optimization device 1, and may be providedin an external storage device such as a database server arranged in acloud, for example. In this case, the action optimization device 1acquires necessary data by accessing the database server of the cloudvia the network NW.

The controller/processor unit 20 includes the above-described hardwareprocessor 20A and program memory 20B, and includes, as softwareprocessing function parts, a GUI management part 21, a policy managementpart 22, an environmental data acquisition part 23, an environmentaldata interpolation part 24, an environment prediction part 25, anenvironment expansion part 26, an environment reproduction part 27, anexploration part 28, and a transmission control part 29. Theseprocessing functions are all realized by causing the hardware processor20A to execute programs stored in the program memory 20B. Thecontroller/processor unit 20 may also be realized in other various formsincluding an integrated circuit such as an application specificintegrated circuit (ASIC) or a field-programmable gate array (FPGA).

The GUI management part 21 provides a GUI for the user, operator, etc.to input an instruction related to processing of the action optimizationdevice 1. For example, the GUI management part 21 displays a GUI on adisplay device (not shown), and receives a user instruction input viathe GUI. In this embodiment, the GUI management part 21 can receive datato be used for training and information specifying a training method viathe GUI, and output them to the policy management part 22 or theexploration part 28.

The policy management part 22 functions as a policy data acquisitionpart, generates policy data based on the information received from theGUI management part 21, and stores the generated policy data in thepolicy data memory 32. The policy management part 22 manages the policydata to arrange relationships between various instructions input via theGUI and models.

The environmental data acquisition part 23 acquires environmental dataincluding information on an environment of a space to be controlled,which is transmitted by the sensor system SS or the building and energymanagement system MS, and stores the acquired environmental data in theenvironmental data memory 31. The environmental data includes, forexample, people flow data acquired by a people flow sensor andtemperature data acquired by a temperature sensor.

The environmental data interpolation part 24 performs time/spaceinterpolation on the acquired environmental data by a preset method. Forexample, the environmental data interpolation part 24 readsenvironmental data for the past 1 hour every hour, and performs timeinterpolation and space interpolation on the read environmental data.

Herein, the time interpolation refers to processing of obtaining(estimating) data of a point intermediate in time with respect to apoint of acquired data, e.g., processing of obtaining data at intervalsof 1 minute in a case where the acquired environmental data is data atintervals of 10 minutes. Similarly, the space interpolation refers toprocessing of obtaining (estimating) data of a spatially intermediatepoint with respect to a point of acquired data, e.g., processing ofobtaining a value corresponding to a position where a sensor is notinstalled from an actually measured value acquired at a position where asensor is installed. Hereinafter, the time interpolation and the spaceinterpolation are collectively referred to as “time/spaceinterpolation”.

The environment prediction part 25 performs future prediction using apreset time-series analysis method based on the acquired environmentaldata to generate environment prediction data.

The environment expansion part 26 performs data augmentation on theacquired environmental data based on a random number. Herein, dataaugmentation means processing of applying a small noise or a mask to aninput side or applying common affine transformation to both the inputside and an output side, and is intended to improve robustness ofprediction processing by performing such processing according to arandom number.

The environment reproduction part 27 has two operation phases, atraining phase and an evaluation phase. In the training phase, theenvironment reproduction part 27 functions as an environmentreproduction model training part, and trains an environment reproductionmodel such that, when a state of an environment and an action forcontrolling the environment are input, a correct answer value for anenvironmental state after the action is output (hereinafter, a modelused in the environment reproduction part 27 is referred to as an“environment reproduction model”). On the other hand, in the evaluationphase, the environment reproduction part 27 predicts, based on anenvironmental state and an action therefor, an environmental state afterthe action by using the trained environment reproduction model.

The exploration part 28 also has two operation phases, i.e., a trainingphase and an evaluation phase. In the training phase, the explorationpart 28 functions as an exploration model training part, and trains anexploration model such that, when an environmental state is input, anaction to be taken next is output (hereinafter, model data used in theexploration part 28 is referred to as an “exploration model”). On theother hand, in the evaluation phase, the exploration part 28 exploresfor, based on an environmental state, a more appropriate action to betaken therefor by using the trained exploration model (explorationprocessing). For example, the exploration part 28 performs prediction(evaluation) of an action a for a state s at each time t to transitionto an optimal next-state s′ during a designated time period, and outputsan optimized action schedule.

The transmission control part 29 functions as an output part, andtransmits (outputs) an exploration result output by the exploration part28 in the evaluation phase to the building and energy management systemMS, etc.

(Operation)

Next, an information processing operation by each part of the actionoptimization device 1 configured as described above will be described.

(1) Acquisition of Environmental Data

FIG. 4 is a flowchart showing an example of a processing procedure andprocessing contents of environmental data acquisition by the actionoptimization device 1.

First, in step S301, the action optimization device 1 acquiresenvironmental data transmitted from the sensor system SS or the buildingand energy management system MS under control of the environmental dataacquisition part 23. In the embodiment, the environmental data includesat least people flow data, and may further include other various dataacquired from the sensor system SS and the building and energymanagement system MS.

The sensor system SS or the building and energy management system MS maytransmit the environmental data at various timings. For example, thesensor system SS or the building and energy management system MS mayacquire data at a predetermined sampling cycle, accumulate the data, anddirectly transmit the accumulated data to the action optimization device1 at regular time intervals (e.g., 1 hour). Alternatively, the actionoptimization device 1 may transmit a data transmission request to thesensor system SS or the building and energy management system MS atregular time intervals or in response to an input of an instruction fromthe user, and the sensor system SS or the building and energy managementsystem MS may transmit the latest environmental data or the accumulateddata to the action optimization device 1 in response to the datatransmission request. Alternatively, the environmental data transmittedfrom the sensor system SS or the building and energy management systemMS may be, for example, accumulated in a database server (not shown) viathe network NW, and the action optimization device 1 may read necessarydata from the database server at regular time intervals or in responseto an input of an instruction from the user.

In step S302, the action optimization device 1 stores the acquiredenvironmental data in the environmental data memory 31.

FIGS. 5A to 5C are diagrams showing examples of the environmental data.

FIG. 5A shows people flow data as an example of the environmental data.In the embodiment, the people flow data includes fields of “time”indicating a time at which a sensor measures a people flow, “identifier”indicating a place where the sensor is installed, and “number of people”measured by the sensor. Various sensors such as a laser sensor, aninfrared sensor, and a camera can be used as the sensor for measuringthe people flow. The field items of the people flow data are not limitedto those shown in FIG. 5A, and various field items may be used, e.g.,the number of people present in a discretionary measurement section(e.g., a 1 m square mesh with an interval of 1 second) per unit time, asthe number of people.

FIG. 5B shows temperature data as an example of the environmental data.In the embodiment, the temperature data includes fields of “time”indicating a time at which a sensor measures a temperature, “identifier”indicating a place where the sensor is installed, and “temperature”measured by the sensor. As the sensor for measuring the temperature,various sensors such as a thermocouple, a temperature measuringresistor, and a thermistor can be used. The field items of thetemperature data are not limited to those shown in FIG. 5B, and variousfield items may be used. For example, a field indicating temperatureaccuracy may be newly provided.

FIG. 5C shows BEMS data as an example of the environmental data. In theembodiment, the BEMS data represents data related to air-conditioningcontrol that can be mainly acquired from the building and energymanagement system MS, and includes fields of “time” indicating a time atwhich the record is written in the management system, “identifier”indicating which of a plurality of air conditioners the recordcorresponds to, “air conditioning” indicating whether to turn on or offthe air conditioner, and “set temperature” indicating the temperature ofair blown from the air conditioner. Again, the field items are notlimited to these, and for example an air supply field indicating anamount of air supplied from the air conditioner may be newly provided.

In addition to the shown field names and values, the environmental datamay also include metadata indicating data names such as “people flow”and “temperature” (not shown). Data having a plurality of fields, suchas BEMS data, may be divided into, for example, air-conditioning dataincluding measurement time, identifier, and air conditioning, andair-conditioning set temperature data including measurement time,identifier, and set temperature, to be managed with finer granularity.

(2) Prediction of Environmental Data

FIG. 6 is a flowchart showing an example of a processing procedure andprocessing contents for predicting environmental data by the actionoptimization device 1 using a preset time-series analysis method. In theembodiment, the environment prediction part 25 predicts environmentaldata for the next one day by using an autoregressive moving average(ARMA) model (see, e.g., Tatsuyoshi Okimoto, “Econometric Time Series ofEconomic and Finance Data”, Asakura Publishing Co., Ltd., Sep. 10, 2017,twelfth print), which is a time series analysis method. The environmentprediction part 25 may be automatically activated at predetermined timeintervals, or may be activated in response to an input of an instructionfrom a user or an operator. In the embodiment, the environmentprediction part 25 is automatically activated for each day to performthe following processing.

First, in step S501, under control of the environment prediction part25, the action optimization device 1 determines whether or not there isany new environmental data stored in the environmental data memory 31since the previous activation. If it is determined in step S501 that nonew data exists, the processing is ended. On the other hand, if it isdetermined in step S501 that new data exists, the process proceeds tostep S502.

In step S502, under the control of the environment prediction part 25,the action optimization device 1 reads the new data and data necessaryfor processing. In the embodiment, the environment prediction part 25reads new data and, if the amount of new data is smaller than orderparameters of the ARMA model, the missing data.

In step S503, the action optimization device 1 performs futureprediction according to a preset prediction equation under the controlof the environment prediction part 25. In the embodiment, theenvironment prediction part 25 uses the ARMA model as the presetprediction equation, estimates weight parameters of the ARMA model, andperforms prediction for the next one day by using the identified model.In the ARMA model, when a predicted value is v_(t), past actuallymeasured values are (v_(t-1), v_(t-2), . . . , v₀), and errors are(ε_(t-1), ε_(t-2), . . . , ε₀), prediction is performed by

$\begin{matrix}{\upsilon_{t} = {{{\phi_{1}\upsilon_{t - 1}} + \ldots + {\phi_{p}\upsilon_{t - p}} + {\theta_{1}\epsilon_{t - 1}} + \ldots + {\theta_{q}\epsilon_{t - q}}} = {{{\sum\limits_{i = 1}^{p}{\phi_{i}\upsilon_{t - i}}} + {\sum\limits_{j = 1}^{q}{\theta_{j}\epsilon_{t - j}}}} = {{{AR}(p)} + {{MA}(q)}}}}} & \left\lbrack {{Equation}1} \right\rbrack\end{matrix}$which is a model of combining an autoregressive model (AR) expressed bya weighted sum of p past values and a moving average model (MA)expressed by a weighted sum of q errors. In the equation, φ_(i) andθ_(i) are parameters representing weights, and p and q are parametersrepresenting orders.

Among them, p and q are estimated in advance by, with all the actuallymeasured values (v_(t-1), . . . , v₀), selecting appropriate ones fromperiods represented by plotting a graph of partial autocorrelation, orautomatically calculating them by such maximum likelihood estimation asto minimize Akaike's Information Criterion (AIC) or Bayesian InformationCriterion (BIC). If optimum values are known in advance, the values maybe written in a config file and the values in the config file may bereferred to. Further, φ_(i) and θ_(i) are automatically calculated bymaximum likelihood estimation that minimizes an error by using (v_(t-1),. . . , v_(t-p)) and (ε_(t-1), . . . , ε_(t-q)) given at the time ofprediction.

In step S504, the action optimization device 1 stores the predictionresult in the environmental data memory 31 as environmental data underthe control of the environment prediction part 25. At this time, a flagindicating that the value is a predicted value may be stored together,and the flag value may be referred to in the subsequent processing toswitch which one of the predicted value and the actually measured valueis used. Herein, the “actually measured value” is an actually measuredvalue (necessarily a time in the past) observed by the sensor system SSor the building and energy management system MS, whereas the “predictedvalue” is a value (necessarily a future time) predicted by theenvironment prediction part 25 or an external device (not shown) basedon an actually measured value. In this embodiment, it is assumed that,at a certain time, when there is only a predicted value, the predictedvalue is used, and when there are both a predicted value and an actuallymeasured value, the actually measured value is used.

The prediction processing is not limited to the above example, and forexample, other prediction methods than the ARMA model may be used. Asthe prediction method, for example, another time-series analysis methodsuch as a seasonal autoregressive integrated moving average model(SARIMA), a regression analysis method such as a multiple regressionanalysis using other types of correlated data, or a deep learning methodsuch as a long-short term memory unit (LSTM) can also be used. Inaddition, an environmental data name and a method to be applied may bedesignated individually by the config file.

By providing the environment prediction part 25 in this way, even in anenvironment in which a predicted value (e.g., a predicted people flow orpredicted weather) cannot be acquired from an external device, eachinstance of processing can be performed using the prediction dataacquired as described above.

(3) Time/Space Interpolation of Environmental Data

FIG. 7 is a flowchart showing an example of a processing procedure andprocessing contents for performing time/space interpolation on acquiredenvironmental data by the action optimization device 1. In theembodiment, the environmental data interpolation part 24 performs thisprocessing.

FIG. 8 shows an example of interpolation target data. In the embodiment,the environmental data interpolation part 24 performs interpolation fromdata (hereinafter, referred to as “point data”) of a specific position(hereinafter, referred to as an “observation point”) corresponding to asensor installation location at intervals of 10 minutes as shown in FIG.8 to data (hereinafter, referred to as “area data”) of all points in atarget area at intervals of 1 minute. The area data may be data obtainedby interpolating three-dimensional points obtained by adding a height totwo dimensions. The environmental data interpolation part 24 may beautomatically activated at predetermined time intervals, or may beactivated in response to an input of an instruction from a user or anoperator. In the embodiment, the environmental data interpolation part24 is automatically activated every hour to perform the followingprocessing.

First, in step S601, under control of the environmental datainterpolation part 24, the action optimization device 1 determineswhether or not there is new environmental data stored in theenvironmental data memory 31 since the previous activation. In theexample of FIG. 8 , the environmental data interpolation part 24determines whether or not values (v_(10,1), . . . , v_(60,n)) obtainedfrom observation points (x₁, . . . , x_(n)) at times (t₁₀, t₂₀, . . . ,t₆₀) after the previous activation time to exist in the environmentaldata memory 31 as environmental data. If it is determined in step S601that there is no new data, the environmental data interpolation part 24ends the processing. On the other hand, if it is determined in step S601that new data exists, the process proceeds to step S602.

In step S602, the action optimization device 1 reads the new data anddata necessary for processing under the control of the environmentaldata interpolation part 24.

Next, in step S603, the action optimization device 1 performs time/spaceinterpolation on the read data according to a preset interpolationequation under the control of the environmental data interpolation part24.

More specifically, the environmental data interpolation part 24additionally reads the value of the time to in step S602, and in stepS603, first applies a linear interpolation method to a set of valuesv _({right arrow over (10)},i)=(v _(0,i) ,v _(10,i) . . . ,v_(60,i))  [Equation 2]of an observation point x_(i) at intervals of 10 minutes to calculate aset of valuesv _({right arrow over (1)},i)(v _(0,i) ,v _(1,1) , . . . ,v_(60,i))  [Equation 3]at intervals of 1 minute.

For example, an interpolation equation of a value v_(k,i) at a timet_(k) satisfying t_(j)<t_(k)≤t_(j+10) is as follows.

$\begin{matrix}{\upsilon_{k,i} = {\upsilon_{j,i} + {\frac{\upsilon_{{j + 10},i} - \upsilon_{j,i}}{t_{j + 10} - t_{j}}\left( {t_{k} - t_{j}} \right)}}} & \left\lbrack {{Equation}4} \right\rbrack\end{matrix}$

The above is calculated for all time intervals {(t₀, t₁₀)), (t₁₀, t₂₀),. . . , (t₅₀, t₆₀)}, and is further applied to all observation points toperform time interpolation.

Next, an inverse distance weighting (IDW) is applied to a set of valuesv _({right arrow over (t)},n)=(v _(t,1) , . . . ,v _(t,n))  [Equation 5]of n observation points at a certain time t (see, e.g., HansWackernagel, translated and edited by Geostatistics Research Committee,translation supervised by Kenji Aoki, “Geostatistics”, MorikitaPublishing Co., Ltd., Aug. 18, 2011, first edition, third print) tocalculate a set of valuesv _(t,{right arrow over (n)}+m)=(v _(t,1) , . . . ,v _(t,n) ,v _(t,n+1), . . . ,v _(t,n+m))  [Equation 6])including m unobserved points. For example, an interpolation equationwhen an unobserved point is u is as follows.

$\begin{matrix}{{\upsilon_{t,u} = \frac{\sum\limits_{i = 1}^{n}{w_{i}\upsilon_{t,i}}}{\sum\limits_{i = 1}^{n}w_{i}}},{w_{i} = \frac{1}{{{u - x_{i}}}^{p}}}} & \left\lbrack {{Equation}7} \right\rbrack\end{matrix}$This is calculated for all the m unobserved points.

The above equation is for obtaining a value of an unobserved point by aweighted average using a reciprocal of a distance as a weight, and p isa parameter for adjusting the degree of influence of neighboring points.The parameter p is automatically calculated by maximum likelihoodestimation that minimizes an error based on x_(i) and v_(t,i) given atthe time of interpolation. If optimum values are known in advance, thevalues may be written in a config file and the values in the config filemay be referred to.

In step S604, under the control of the environmental data interpolationpart 24, the action optimization device 1 stores thetime/space-interpolated result obtained by the above processing in theenvironmental data memory 31 as area data of the environmental data.

For the time/space interpolation method, other methods may be designatedsuch as using spline interpolation for the time interpolation and usingkriging for the space interpolation, and an environmental data name anda method to be applied may be designated individually by the configfile, etc. An interpolation interval in the time interpolation, thenumber and positions of unobserved points to be interpolated in thespace interpolation, the processing order of the time interpolation andthe space interpolation, etc. may be discretionarily set through theconfig file, etc.

Note that the environment prediction part 25 and the environmental datainterpolation part 24 may perform each instance of processing not atregular time intervals but each time storage in the environmental datamemory 31 is detected. In addition, the activation order of theenvironment prediction part 25 and the environmental data interpolationpart 24 is discretionary, and the environmental data interpolation part24 may perform interpolation on a result of the environment predictionpart 25 or the environment prediction part 25 may perform predictionusing a result of the environmental data interpolation part 24.

(4) Acquisition of Policy Information

Next, acquisition of policy information input via a GUI will bedescribed.

FIG. 9 shows a policy input screen 210 displayed on a display part, etc.(not shown) as an example of a GUI for inputting instructions (policies)related to various processing that is provided by the GUI managementpart 21 of the action optimization device 1. The GUI management part 21can fetch a policy instruction input by a user or an operator using aninput device (not shown) such as a keyboard, a mouse, or a touch panelvia such a policy input screen 210. The policy input screen 210 includestext boxes 211 to 215 for inputting a state, an action, a rewardfunction, a reproduction method, and an exploration method, and atransmission button 216 for ending the input, but the contents are notlimited thereto.

An action field 212 is a field for inputting a control target inexploration processing. In the present embodiment, “air conditioning”represents an operating status of an air conditioner of ON/OFF, and itis assumed that one of several controls is optimized, but the embodimentis not limited thereto. For example, in the action field 212, anotheraction such as a set temperature may be set, or an operating status ofeach of a plurality of air conditioners installed in the same targetspace may be set.

In addition, in the action field 212, a virtual action such as groupingcontrol targets in several units or registering a predeterminedprocedure in advance may be set. Thus, for example, a plurality of airconditioners may be grouped according to whether they belong to apriority section with a large number of users or a normal sectionwithout a large number of users, and virtual actions in units of groupssuch as a “priority air conditioning group” and a “normal airconditioning group” may be set. The grouping method is not limitedthereto, and the virtual action can be set in various units such as asection easily affected by the outside air or solar radiation, a sectionincluding a heat source such as a kitchen, and a section in which anevent is held.

Further, a virtual action including a series of operations for realizinga predetermined function may be set. For example, in a case where thereis a procedure determined at the time of manufacturing such as graduallyreducing the air volume when air conditioning is stopped, a virtualaction of “air conditioning stop” for performing an operation accordingto the procedure can be set. As other examples of such a virtual action,it is also possible to set procedures such as “startup preparation” forperforming an operation across a plurality of devices having adependency relationship such as operating a heat source before an airconditioning operation so as to adjust a water amount and a watertemperature of a heat storage layer, “dehumidification mode” forperforming an operation to increase a dehumidification effect bylowering a supply temperature and reducing an air supply amount, and“airflow control” for performing an operation to generate airflow byusing an actuator such as a circulator or an air curtain or changing anindoor pressure balance by adjusting a ventilation amount and an airsupply amount. However, the embodiment is not limited to these specificexamples, and various procedures can be adopted as the virtual actionsaccording to the purpose of use, the environment of use, etc. Thevirtual action may be related to an operation for a single controltarget, an operation for a plurality of control targets of the sametype, or an operation for a plurality of control targets of differenttypes.

A state field 211 is a field for inputting a name of environmental datathat is affected when an action is changed. In the figure, people flow,temperature, humidity, and outside air are exemplified to be used, butthe input data is not limited thereto and may include, for example,solar radiation amount.

The reproduction method field 213 is a field for inputting a method forthe environment reproduction part 27 to predict a relationship betweenan action and a state. In the figure, using a method of a deep learningconvolutional LSTM (see, e.g., Xingjian Shi, et al., Convolutional LSTMnetwork: A Machine Learning Approach for Precipitation Nowcasting. NIPS,2015) used for a short-term weather forecast, etc. is exemplified. Inaddition, it is assumed that, by using the method, a model that has anaction a and a state s as inputs and outputs a next-state s′ after 1hour as an output has already been defined and stored as model data.However, the embodiment is not limited thereto, and a method such asoutputting a next-state s′ after a discretionary time (e.g., after 10minutes or 1 day) passes, using another method such as a multipleregression analysis, or cooperating with a physical simulator such as acomputational fluid dynamics simulator may be adopted.

The exploration method field 214 is a field for inputting a method forthe exploration part 28 to perform an exploration. In the figure, usinga method of Deep Q-Network in deep reinforcement learning isexemplified, but the embodiment is not limited thereto. Anotherreinforcement learning method such as dynamic programming or TD learningmay be used (see, e.g., Csaba Szepesvari, translator representative &edited by Sotetsu Koyamada, translation supervised by Shinichi Maeda &Masanori Koyama, “Intensive Learning: Reinforcement Learning—BasicTheory and Algorithm”, Kyoritsu Shuppan Co., Ltd., Sep. 25, 2017, firstedition, first print).

As for the reproduction method field 213 and the exploration methodfield 214, it is exemplified that a corresponding model or simulator isregistered as model data in advance, and the name of the model is inputthereto. However, the embodiment is not limited thereto, and a programmay be directly described, for example.

The reward function field 215 is a field for inputting an evaluationequation for an action determined by the exploration part 28 accordingto the method of the exploration method field 214. In the figure, whenone cycle of training is completed, a total energy consumption reductionamount of one cycle is designated to be returned as a reward r, and inthe other cases, a sum of an energy reduction amount (reward1) at thattime and a negative value (reward2) of a difference between outside airand a temperature is designated to be returned as the reward r. In thelatter case, at a certain time t, the higher the energy consumptionreduction, the higher a value of reward1, and thereby an effect oflowering a peak value of an electric power from the viewpoint of energysaving is expected, and the lower a difference between outside air and aroom temperature, the higher a value of reward2, and thereby an effectof preventing heat shock or cold shock from the viewpoint of comfort isexpected. The embodiment is not limited thereto, and in an evaluationequation of a reward function, for example, a comfort index may becalculated from temperature and humidity values, or an amount of heatgenerated by a people flow may be taken into consideration.

Further, in an evaluation equation of a reward function, a generallyknown operation leading to energy saving may be highly evaluated.Examples of such an operation leading to energy saving include a peakcut/shift by an intermittent operation of an air conditioner or athinning-out operation of unused sections, pre-stop of a heat source bystopping the heat source earlier than a business closing time andperforming an air conditioning operation using only retained cold andhot water, natural ventilation utilization when an outdoor temperatureis more comfortable than an indoor temperature, redundancy reduction ofan air conditioning function for adjusting a set temperature to obtain asufficient air conditioning effect while reducing an inlet/outlettemperature difference of cold and hot water, a large temperaturedifference of conversely increasing an inlet/outlet temperaturedifference of cold and hot water to reduce an amount of water or airused by an air conditioner, and outside air inflow prevention by aircurtain activation in the vicinity of an opening when a pressuredifference due to an indoor/outdoor temperature difference is large.However, the embodiment is not limited thereto, and various operationsaccording to the purpose of use, the environment of use, etc. can beconsidered. Furthermore, these may be combined and designated in theform of a weighted sum according to the degree of importance.

Although the reward function field 215 directly describes the program,the embodiment is not limited thereto, and an evaluation equation may beregistered as model data in advance and the name of the model data maybe described.

In FIG. 9 , when the transmission button 216 is pressed, the GUImanagement part 21 determines that the input has been completed andoutputs the above content to the policy management part 22.

(5) Generation of Policy Data

FIG. 10 is a flowchart showing an example of a processing procedure andprocessing contents for generating policy data by the actionoptimization device 1. In the embodiment, the policy management part 22receives policy information output by the GUI management part 21, andgenerates policy data based on the policy information.

In step S901, under control of the policy management part 22, the actionoptimization device 1 receives the policy information output from theGUI management part 21 as an argument, and extracts the action, state,reward function, reproduction method, and exploration method fields fromthe argument.

In step S902, under the control of the policy management part 22, theaction optimization device 1 collectively stores the informationextracted from the argument in the policy data memory 32 as policy data.

In step S903, under the control of the policy management part 22, theaction optimization device 1 outputs a training instruction to theenvironment reproduction part 27, and ends the processing. The traininginstruction may include policy data, or may include a notificationindicating that policy data is newly stored in the policy data memory32.

FIG. 11 is a diagram showing an example of policy data generated by thepolicy management part 22. In the embodiment, the policy data includesfields of “identifier” for uniquely identifying each policy, “action”indicating control of a target space, “state” indicating a name ofenvironmental data affected when an action is changed, “reward function”indicating an evaluation equation used by the exploration part 28 in atraining phase, “reproduction method” indicating a model used by theenvironment reproduction part 27, and “exploration method” indicating amodel used by the exploration part 28. As for the reward function,reproduction method, and exploration method, a program which isconverted into an executable state (hereinafter, referred to as a“binary”) may be described, or a name of model data may be described. Inaddition, a binary may be stored in the data memory 30 as model data,and the name or identifier of the model data may be used.

Hereinafter, for each description content of the policy data, when atime t is given, a value of environmental data corresponding to theaction field is referred to as an action a, a value of environmentaldata corresponding to the state field is referred to as a state s, and astate when one action a is selected from a plurality of actions aassumed in the state s and the time is advanced by one by performing theone action a is referred to as a next-state s′; further, the content ofthe reward function field is referred to as a reward function R, and avalue obtained by inputting the action a, state s, and next-state s′ atthe time t to the reward function R is referred to as a reward r.

For example, when “air conditioning” is described in the action field,an air conditioning field of air conditioning data stored asenvironmental data is extracted and used as an action a at each time t.A data name and a field name may be individually set in the form of, forexample, “(BEMS data, air conditioning)”. In a case where there are aplurality of air conditioners, an action at each time cannot be uniquelyobtained only by the air conditioning field. Thus, the identifier fieldis also included automatically as a target, and a pair of the identifierfield and the air conditioning field is read as an action a. The fieldname may be clearly specified as, for example, “(BEMS data, [airconditioning, identifier])”.

For each of the reproduction method, exploration method, and rewardfunction, when a binary is described, the binary may be evaluated andstored in the data memory as model data, and may be overwritten with theidentifier or name of the model data. Note that the policy managementpart 22 is not limited to using the information from the GUI managementpart 21 as a startup trigger. For example, a functional part thatreceives a request including necessary parameters from the building andenergy management system MS may be newly provided, and the above policydata generation processing may be performed using the request as atrigger.

(6) Training of Environment Reproduction Model

FIG. 12 is a flowchart showing an example of a processing procedure andprocessing contents for training, by the action optimization device 1,from past data how an environment in a target space changes when anaction for controlling the environment is changed. In the embodiment,under control of the environment reproduction part 27, the actionoptimization device 1 receives a training instruction from the policymanagement part 22 and starts training of an environment reproductionmodel (the training phase). In the training phase, the environmentreproduction part 27 functions as an environment reproduction modeltraining part, acquires policy information as an argument from thepolicy management part 22 or the policy data memory 32, and trains anenvironment reproduction model for predicting a next-state s′ when anaction a is performed in a state s at a time t by using data of theentire period. The training may be performed every time a facilitylayout is changed.

First, in step S1101, under the control of the environment reproductionpart 27, the action optimization device 1 extracts policy data from anargument output from the policy management part 22.

In step S1102, under the control of the environment reproduction part27, the action optimization device 1 reads an environment reproductionmodel corresponding to the content described in the reproduction methodfield.

In step S1103, under the control of the environment reproduction part27, the action optimization device 1 randomly selects any time from theentire period and sets the selected time as a time t.

In step S1104, under the control of the environment reproduction part27, the action optimization device 1 sends a request for reading theaction a, state s, and next-state s′ at the time t to the environmentexpansion part 26, and obtains the data. However, this step is optional,and the environment reproduction part 27 may be configured to directlyread the action a, state s, and next-state s′ at the time t from thedata memory 30. Processing of the environment expansion part 26 will bedescribed later.

In step S1105, under the control of the environment reproduction part27, the action optimization device 1 inputs the state s and the action ato the read environment reproduction model, calculates a differencebetween a state fs, which is an output predicted value, and thenext-state s′, which is a correct answer value, and updates eachparameter of the environment reproduction model using a publicly-knowntechnique such as backpropagation (see, e.g., C. M. Bishop, translationsupervised by Hiroshi Genta et al., “Pattern Recognition and MachineLearning, Vol. 1”, Maruzen Publishing Co., Ltd., Jul. 30, 2016, seventhprint).

In step S1106, under the control of the environment reproduction part27, the action optimization device 1 determines whether or not thedifference of the above parameter update is equal to or less than apredetermined threshold value. If it is determined that the differenceis not equal to or less than the threshold value, the environmentreproduction part 27 returns to step S1103 and repeats the processing ofsteps S1103 to S1105. If it is determined in step S1106 that thedifference of the parameter update is equal to or less than thethreshold value, the process proceeds to step S1107.

In step S1107, under the control of the environment reproduction part27, the action optimization device 1 stores the environment reproductionmodel in which the parameters are updated in the model data memory 33 asmodel data, and ends the processing.

A start time field and an end time field may be newly provided in thepolicy input screen 210 provided by the GUI management part 21, theinput by the user may be received and output to the policy managementpart 22, and the policy management part may further pass the input valueto the environment reproduction part 27 so as to perform training usingdata of the designated period.

By the training using the area data interpolated by the environmentaldata interpolation part 24, it is possible to estimate an influence of acontrol change taking into account an interaction existing in the targetspace.

(7) Data Augmentation Processing

As described above, the environment reproduction part 27 can use datasubjected to augmentation processing in the training processing.

FIG. 13 is a flowchart showing an example of a processing procedure andprocessing contents of data augmentation by the action optimizationdevice 1. In the embodiment, data augmentation processing is performedby the environment expansion part 26 of the action optimization device1. This processing is optional processing that can be used in thetraining phase by the environment reproduction part 27.

In step S1201, under control of the environment expansion part 26, theaction optimization device 1 extracts a time t from the argument.

In step S1202, under the control of the environment expansion part 26,the action optimization device 1 reads the action a, state s, andnext-state s′ at the designated time t.

In step S1203, under the control of the environment expansion part 26,the action optimization device 1 performs data augmentation processingbased on random numbers within a range that does not destroy arelationship between the action a and the state s (hereinafter,collectively referred to as an “input side”) as inputs and thenext-state s′ (hereinafter, referred to as an “output side”) as anoutput in the environment reproduction model.

In step S1204, under the control of the environment expansion part 26,the action optimization device 1 returns the data-augmented [state s,next-state s′, action a] to a caller (here, the environment reproductionpart 27) as a processing result.

Note that the environment expansion part 26 is not limited to beingactivated in response to a request from the environment reproductionpart 27. For example, like the environment prediction part 25 and theenvironment reproduction part 27, the environment expansion part 26 maybe activated at regular time intervals or by detecting storage in thedata memory 30, and may store the augmentation data subjected to thedata augmentation processing in the environmental data memory 31 asenvironmental data. At this time, an augmentation flag may be given tothe environmental data, and the flag value may be referred to to switchwhich of the augmentation data and non-augmentation data to use.

In the embodiment, an environmental data name and a data augmentationmethod to be applied are individually set in the config file in advance,and data augmentation can be performed according to the contents of theconfig file.

Further, for example, when a plurality of air conditioners aredesignated as actions, a list of area information including positionsand sizes of predefined space ranges (hereinafter, referred to as“air-conditioned areas”) in a facility served by the respective airconditioners may be described in the config file, area data may bedivided by determining to which air-conditioned area the data of eachpoint of the area data belongs based on the config file, and dataaugmentation may be performed in divided data units.

As described above, for example, in a case where noise is added totemperature data on the input side, it is possible to achieve suchtraining of a reproduction model with high robustness as to permitfluctuation of an actually measured value of the temperature data. Inaddition, in a case where common mask processing is applied to both theinput side and the output side in area units, the interaction betweenareas can be taken into account or the areas can be decoupled from eachother to achieve training. In a case where mask processing is performedsuch that only one area remains, as training for each area progressesusing only data of the area itself, prediction by separating therelationship between the areas can be performed. Thus, even if only datawith the same control timing of a plurality of air conditioners can beobserved, for example, prediction in a case of controlling the airconditioners individually can be performed. In addition, in a case wheremask processing is performed such that a plurality of areas remain, astraining for each area progresses using not only data of the area itselfbut also data of other areas, prediction taking into account therelationship between the areas can be performed. Thus, for example, itbecomes possible to consider area characteristics such as cold/warm airinflow or heat accumulation being likely to occur. Since training inwhich the above is mixed progresses by using random numbers, it ispossible to perform natural prediction for various variations with asmall amount of data.

(8) Evaluation using Environment Reproduction Model

FIG. 14 is a flowchart showing an example of a processing procedure andprocessing contents for predicting (herein, also referred to as“evaluating”) a next-state s′ when performing an action a in a state sat a designated time t by the action optimization device 1 using atrained environment reproduction model. In the embodiment, under thecontrol of the environment reproduction part 27, the action optimizationdevice 1 receives an evaluation instruction from the exploration part 28and starts evaluation processing (the evaluation phase).

In step S1301, under the control of the environment reproduction part27, the action optimization device 1 receives, as an argument,information transmitted from the exploration part 28 together with theevaluation instruction, and extracts policy data, the time t, a periodtr, and the action a from the argument.

In step S1302, under the control of the environment reproduction part27, the action optimization device 1 determines whether or not theextracted action a is empty. If it is determined that the action is notempty, the process proceeds to step S1303. On the other hand, if it isdetermined in step S1302 that the action a is empty, the processproceeds to step S1304 to determine the above evaluation instructionfrom the exploration part 28 to be an initial state acquisition command,setting the state s at the time t as the next-state s′, and setting 0 asthe reward r, and the process proceeds to step S1308.

In step S1303, under the control of the environment reproduction part27, the action optimization device 1 reads the trained environmentreproduction model and a reward function R based on the extracted policydata.

Subsequently, in step S1305, under the control of the environmentreproduction part 27, the action optimization device 1 reads the state sat the time t, and if the next-state s′ predicted in the previousprocessing remains in the memory, the action optimization device 1 usesthe next-state s′ as the state s.

In step S1306, under the control of the environment reproduction part27, the action optimization device 1 inputs the state s and the action ato the environment reproduction model, and sets an output predictedstate fs as the next-state s′.

In step S1307, under the control of the environment reproduction part27, the action optimization device 1 inputs the state s, next-state s′,and action a to the reward function R to acquire the reward r.

In step S1308, under the control of the environment reproduction part27, the action optimization device 1 outputs the next-state s′ and thereward r to a request source (here, the exploration part 28) as anending procedure of the processing.

In step S1309, under the control of the environment reproduction part27, the action optimization device 1 stores the next-state s′ in thememory so that, when prediction at a time t+1 is performed next,processing can be performed by using a predicted value, instead of anactually measured value, as the state s at the time t. However, if thetime t exceeds the period tr, the prediction at the time t+1 is notrequested, and thus the next-state s′ is not stored.

For the next-state s′ to be output to the request source, when a fields_(i) that is not affected by the change of the action a is known inadvance, a pair of the policy data identifier and the state field s_(i)may be described in the config file in advance, and the field s_(i) maybe overwritten with a value of s_(i) included in the next-state s′ atthe time t existing in the data memory 30, instead of the valuecalculated by the environment reproduction model, based on the configfile, and returned. In the training phase, training may be performedafter the s_(i) field is deleted from the output of the environmentreproduction model.

(9) Acquisition of Exploration Instruction

Next, acquisition of an exploration instruction input via a GUI will bedescribed.

FIG. 15 shows an input screen 220 of an exploration instructiondisplayed on a display part, etc. (not shown) as an example of a GUI forinputting an instruction related to an exploration, which is provided bythe GUI management part 21 of the action optimization device 1. The GUImanagement part 21 can fetch an exploration instruction input by a useror an operator using an input device (not shown) such as a keyboard, amouse, or a touch panel via such an exploration instruction input screen220. The exploration instruction input screen 220 includes text boxes221 to 224 for inputting a start time, an end time, policy data, and atransmission destination, a radio button 225 for inputting a type, and atransmission button 226 for ending the input, but is not limitedthereto.

A start time field 221 and an end time field 222 are fields forinputting data of which period is used.

A policy data field 223 is a field for inputting an identifier of policydata output by the policy management part.

A type field 225 is a field for selecting which of a traininginstruction and an evaluation instruction is transmitted to theexploration part 28.

A transmission destination field 224 is a field for, when an input isadditionally required when the evaluation instruction is selected in thetype field 225, inputting a destination to which the exploration resultoutput by the evaluation phase is transmitted. In the present example,it is assumed that the building and energy management system MS includesan API for receiving the exploration result through an HTTPcommunication and a URL of the API is input, but the example is notlimited thereto. For example, a communication method by a specificprotocol may be described.

When the transmission button 226 is pressed and the input is completed,the GUI management part 21 outputs the above contents to the explorationpart 28. At this time, either the training phase or the evaluation phaseis automatically selected according to the input value of the type field225.

(10) Training of Exploration Model

FIG. 16 is a flowchart showing an example of a processing procedure andprocessing contents for training of an exploration model used for anexploration by the action optimization device 1. In the embodiment,under control of the exploration part 28, the action optimization device1 starts training of an exploration model in response to a traininginstruction from the GUI management part 21 (the training phase). In thetraining phase, the exploration part 28 functions as an explorationmodel training part, acquires the information transmitted from the GUImanagement part 21 as an argument, and performs training of anexploration model for predicting an action a for a state s at each timet to transition to an optimal next-state s′ using data of a designatedperiod.

In step S1501, under the control of the exploration part 28, the actionoptimization device 1 extracts policy data, a start time, and an endtime from the argument.

In step S1502, under the control of the exploration part 28, the actionoptimization device 1 reads an exploration model corresponding to acontent described in an exploration method field of the extracted policydata.

In step S1503, under the control of the exploration part 28, the actionoptimization device 1 further randomly selects any day between the starttime and the end time, and sets 00:00 of that day as the time t.

In step S1504, under the control of the exploration part 28, the actionoptimization device 1 outputs the time t, an empty action a, and aperiod tr including the start time and the end time to the environmentreproduction part 27, and acquires an initial state s. Note that thetime may not be 00:00, and for example, when optimization at night isnot necessary, the business start time (09:00, etc.) of the facility maybe designated.

In step S1505, under the control of the exploration part 28, the actionoptimization device 1 inputs the state s to the exploration model andacquires an action a to be taken next. When acquiring the action a, notonly may the best candidate selected by the exploration model beselected from a plurality of candidates, but also a random candidate maybe selected with a certain probability in order to proceed with anunknown exploration.

Subsequently, in step S1506, under the control of the exploration part28, the action optimization device 1 outputs the time t, the action a,and the period tr to the environment reproduction part 27, and acquiresa next-state s′ and a reward r.

In step S1507, under the control of the exploration part 28, the actionoptimization device 1 updates each parameter of the exploration model byusing a publicly known technique such as backpropagation using a resultincluding the time t, state s, next-state s′, reward r, and action a(see, e.g., C. M. Bishop, translation supervised by Hiroshi Genta etal., “Pattern Recognition and Machine Learning, Vol. 1”, MaruzenPublishing Co., Ltd., Jul. 30, 2016, seventh print). Instead of updatingthe parameters each time, the result may be temporarily stored in amemory to update the parameters in a batch process using a plurality ofresults, or a look-ahead reward r′=r₁+r₂ may be obtained using resultsat consecutive times t₁ and t₂ to update the parameters with the timet₁, a state s₁, a next-state s₂′, and the reward r′.

In step S1508, under the control of the exploration part 28, the actionoptimization device 1 determines whether or not the time t exceeds theend time. If it is determined that the time t does not exceed the endtime, the process proceeds to step S1509, the time t is advanced by one,the next-state s′ is substituted for the state s, and then theprocessing of steps S1505 to S1507 is repeated.

On the other hand, if it is determined in step S1508 that the time texceeds the end time, the process proceeds to step S1510.

In step S1510, under the control of the exploration part 28, the actionoptimization device 1 determines whether or not a parameter updatedifference is equal to or less than a predetermined threshold value. Ifit is determined in step S1510 that the parameter update difference isnot equal to or less than the threshold value, it is determined thatthere is still room for training, and the process proceeds to step S1503to repeat training based on data on another day. If it is determined instep S1510 that the parameter update difference is equal to or less thanthe threshold value, the process proceeds to step S1511.

In step S1511, under the control of the exploration part 28, the actionoptimization device 1 stores the parameter-updated exploration model inthe model data memory 33 as model data, and ends the processing.

In this way, under the control of the exploration part 28, the actionoptimization device 1 performs training of the exploration model whilecommunicating with the environment reproduction part 27 in the trainingphase.

(11) Evaluation Using Exploration Model

FIG. 17 is a flowchart showing an example of a processing procedure andprocessing contents for performing an exploration by the actionoptimization device 1 using a trained exploration model. In theembodiment, under the control of the exploration part 28, the actionoptimization device 1 receives an evaluation instruction output by theGUI management part 21 and starts evaluation processing (the evaluationphase). For example, the exploration part 28 can be configured toperform evaluation processing for each day, and generate an explorationresult for the next day.

In the evaluation phase, processing is basically performed in the sameprocedure as that of the training phase, but the evaluation phase isdifferent from the training phase in that an action a for a state s totransition to an optimal next-state s′ is evaluated while continuouslyadvancing a time t from a start time to an end time, the explorationmodel is not updated, and a list of actions a acquired before the end ofprocessing is stored as an exploration result. The exploration resultcan also include metadata such as an identifier of policy data used whengenerating the exploration result.

In step S1601, under the control of the exploration part 28, the actionoptimization device 1 extracts policy data, a start time, an end time,and a transmission destination from an argument.

In step S1602, under the control of the exploration part 28, the actionoptimization device 1 reads a trained exploration model corresponding toa content described in an exploration method field of the extractedpolicy data.

In step S1603, under the control of the exploration part 28, the actionoptimization device 1 further sets the start time as a time t.

In step S1604, under the control of the exploration part 28, the actionoptimization device 1 outputs the time t, an empty action a, and aperiod tr including the start time and the end time to the environmentreproduction part 27, and acquires an initial state s.

In step S1605, under the control of the exploration part 28, the actionoptimization device 1 inputs the state s to the exploration model, andacquires an action a to be taken next.

In step S1606, under the control of the exploration part 28, the actionoptimization device 1 then outputs the time t, action a, and period trto the environment reproduction part 27, and acquires a next-state s′and a reward r.

In step S1607, under the control of the exploration part 28, the actionoptimization device 1 determines whether or not the time t exceeds theend time. If it is determined that the time t does not exceed the endtime, the process proceeds to step S1608, the time t is advanced by one,the next-state s′ is substituted for the state s, and then theprocessing of steps S1605 to S1606 is repeated. On the other hand, if itis determined in step S1607 that the time t exceeds the end time, theprocess proceeds to step S1609.

In step S1609, under the control of the exploration part 28, the actionoptimization device 1 stores a list of the acquired actions a in theexploration result memory 34 as an exploration result.

In step S1610, under the control of the exploration part 28, the actionoptimization device 1 outputs, to the transmission control part 29, theexploration result or a notification indicating that the explorationresult should be transmitted, together with the transmission destinationextracted from the argument.

In this way, the exploration part 28 performs exploration processingwhile communicating with the environment reproduction part 27 even inthe evaluation phase.

The training phase and the evaluation phase of the exploration part 28are not limited to being activated based on the information from the GUImanagement part 21. For example, the phases may be performed atpredetermined time intervals, or the exploration part 28 itself maydetect an event such as storage in the data memory 30 to performcorresponding processing. In this case, a set of necessary parameterssuch as policy data, a start time, an end time, and a transmissiondestination, an activation phase, and the event can be described in aconfig file.

(12) Output of exploration Result

FIG. 18 is a flowchart showing an example of a processing procedure andprocessing contents in which the action optimization device 1 transmitsan exploration result to the building and energy management system MS.In the embodiment, the action optimization device 1 executes thisprocessing under control of the transmission control part 29.

In step S1701, under the control of the transmission control part 29,the action optimization device 1, by using the information output fromthe evaluation phase of the exploration part 28 as an argument, extractsa transmission destination and an exploration result from the argument.

In step S1702, under the control of the transmission control part 29,the action optimization device 1 performs processing of transmitting theexploration result to the designated transmission destination.

However, the output of the exploration result is not limited to thisprocedure, and may be activated in response to an exploration resultacquisition request from the building and energy management system MS orby a user instruction via a command transmission screen newly providedin the GUI management part 21.

FIG. 19 is a diagram showing an example of the exploration result to beoutput. The exploration result includes fields of “time” indicating thetiming of changing a control and “action” indicating how to change eachof several controls. However, each field item of the exploration resultis not limited thereto, and for example, all times may be output insteadof summarizing only the change timings, or fields having namescorresponding to identifiers may be increased or decreased in the samenumber as the number of controls.

As described above, in this embodiment, at a certain time, when only apredicted value predicted based on an actually measured value can beacquired, the predicted value is used, and when both a predicted valueand an actually measured value can be acquired, the actually measuredvalue is used. Cases where a predicted value is used include, forexample, the following.

-   -   Case where the exploration part 28 performs action optimization        of a future period in the evaluation phase (e.g., a case of        calculating an exploration result for one day from that point in        time).    -   Case where the environment reproduction part 27 knows that the        accuracy of an existing predicted value is higher than that of        its own prediction in the evaluation phase (e.g., a case where a        predicted outside air temperature has been acquired from the        Meteorological Agency). In the latter case, since an outside air        temperature on the output side of the environment reproduction        model is not used, the outside air temperature data is deleted        from the output of the environment reproduction model through        the config file and then each of the training phase and the        evaluation phase is executed in the environment reproduction        part 27.

Example 2

FIG. 20 is a diagram showing a second example of an overallconfiguration of a system including the action optimization device 1according to the embodiment of the present invention. In this example,the action optimization device 1 is assumed to detect dirt conditions ina target area using a dust sensor and optimize cleaning in the targetarea, as an action for controlling an environment in a target space. Ascompared to the system shown in FIG. 1 , the sensor system SS comprises,in addition to the sensors Ss1, . . . , Ssn, a vacuum cleaner (or asensor provided in the vacuum cleaner) Sm1, an air cleaner (or a sensorprovided in the air cleaner) Sm2, and a camera Sm3 as additionalsensors. By these additional sensors, for example, environmental dataincluding information indicating dirt such as data obtained by measuringan amount of dust sucked in by the vacuum cleaner with an infraredsensor, a value of a dust sensor of the air cleaner, and data obtainedby detecting a dirty portion from an image of the camera is acquired.

FIG. 21 is a diagram showing a cleaning optimization policy input screen250 as an example of a GUI that can be provided by the GUI managementpart 21 of the action optimization device 1 in the system of Example 2.The policy input screen 250 includes text boxes 251 to 255 for inputtinga state, an action, a reward function, a reproduction method, and anexploration method, and a transmission button 256 for ending the input,but is not limited thereto.

The policy input screen 250 is different in input contents from thepolicy input screen 210 shown in FIG. 9 in that a cleaning state (i.e.,cleaning is performed at the time) is input as a control in an actionfield 252, and dirt and a people flow are input in a state field 251. Ina reward function field 255, such inputs are exemplified as to return atotal dirt removal amount of 1 day in a case where 1 training cycle iscompleted, and in the other cases, +1 or −1 (0 when cleaning is notperformed) depending on a people flow amount when cleaning is performed.In addition, such inputs are exemplified as to use multiple regressionas a reproduction model in a reproduction method field 253, and dynamicprogramming as an exploration model in an exploration method field 254.However, the inputs are not limited thereto, and, for example, acleaning intensity indicating how intensively cleaning should beperformed may be designated in the action field 252, the material of afloor may be added to the state field 251, or an item for minimizing thetime required for cleaning may be added to the reward field 255.

In addition, in the system according to Example 2, the configuration andeach operation of the action optimization device 1 are the same as thoseof Example 1, and thus detailed description thereof will be omitted.

(Effect)

As described above in detail, in an embodiment of the present invention,the action optimization device 1 acquires environmental data related toa space to be controlled from the sensor system SS or the building andenergy management system MS, and performs time/space interpolation onthe acquired environmental data. Based on this time/space-interpolatedenvironmental data, the environment reproduction part 27 trains anenvironment reproduction model such that, when a state of an environmentand an action for controlling the environment are input, a correctanswer value of an environmental state after the action is output, andpredicts a change in environment (a next-state s′ when an action a isperformed in a state s at a time t) based on the trained environmentreproduction model. On the other hand, the exploration part 28 trains anexploration model for predicting an action a for a state s at each timet to transition to an optimal next-state s′ while communicating with theenvironment reproduction part 27, and acquires and outputs a list ofappropriate actions to be taken for respective states based on thetrained exploration model while also communicating with the environmentreproduction part 27.

Therefore, according to the embodiment, since training and evaluationare performed by the environment reproduction part 27 and theexploration part 28 after a change in future environmental data ispredicted in advance based on actually acquired environmental data, itis possible to realize optimization of an action to be taken by highlyreliable feedforward control. Thus, in the management of a building or afacility such as an office building, an appropriate control schedule canbe obtained for any management target such as air conditioning orcleaning, and an efficient management operation that responds to achange in environment in a space in a timely manner is possible.

In addition, since the environmental data interpolation part 24 performstime/space interpolation on the acquired environmental data, it is alsopossible to perform training and evaluation based on a control effect ofan entire target space taking into account changes in localenvironmental conditions. Thus, a problem caused by non-uniformity ofenvironmental conditions such as heat accumulation is solved. Inaddition, since an influence from an adjacent space is also taken intoconsideration, it is possible to realize an exploration with higheraccuracy even taking into account an interaction existing in the targetspace.

Furthermore, the environment prediction part 25 is provided to performfuture prediction from the acquired environmental data so as to obtainenvironment prediction data. Accordingly, even in an environment inwhich a predicted value (e.g., a predicted people flow or predictedweather) cannot be acquired from an external device, etc., it ispossible to perform each instance of processing including an explorationusing the environment prediction data.

Since a policy of action optimization, a startup timing of a trainingphase, a startup timing of an evaluation phase, a target period, etc.can be easily set by the GUI management part 21, flexible controlaccording to the situation of the building or facility can be performed.In addition, various instructions input via the GUI are managed aspolicy data by the policy management part 22, and a relationship betweendesignated parameters (reproduction method, environment reproductionmodel, reward function, etc.) and model data is appropriately arranged.

Further, since apparent data can be increased by the environmentexpansion part 26, it is possible to shorten the time required for theenvironment reproduction part 27 or the exploration part 28 to acquire asufficient amount of environmental data before starting training. Inaddition, since robustness of the prediction processing is improved bythe data augmentation using random numbers, reliability of theenvironmental data subjected to the augmentation processing can beimproved.

Furthermore, as a control target in the exploration processing of theaction optimization device 1, in addition to a switching control such asON/OFF of an air conditioner, various targets such as detailed settingof a set temperature, etc. and an operating status of each of aplurality of air conditioners can be set, and flexible control accordingto a purpose or an environment can be realized. In addition, an actionof a group unit with respect to a control target group obtained bygrouping control targets in advance can be set as a target of theexploration processing. Thus, proper control taking into account anactual environment can be performed by grouping based on a discretionarycriterion, e.g., a section with many users and a section with few users,a section with a large amount of user movement and a section with asmall amount of user movement, a section easily affected by outside airor solar radiation and a section hardly affected by outside air or solarradiation, a section with a heat source such as a kitchen and a sectionwithout a heat source, a section where an event is held and a sectionwithout an event, and a section where food and drink are provided and asection without food and drink. Moreover, a virtual action including aseries of operations for performing a predetermined function may be setas a target of the exploration processing. In this way, for example, ina case where there is a procedure or an operation mode set in advance atthe time of manufacturing, such as “startup preparation” or“dehumidification mode” in an air-conditioning device, it is possible toperform a more efficient control by collectively handling such a seriesof operations as a virtual action.

Furthermore, generally known operations leading to energy saving can behighly evaluated in a reward function. As a result, it is possible toexplore for and control an appropriate action by positively utilizingthe operations leading to energy saving.

Other Embodiments

The present invention is not limited to the above-described embodiment.For example, it has been described in the embodiment that an actuallymeasured value of environmental data is acquired, and is subjected totime/space interpolation to be used for various processing. However, apredicted value obtained in the past may be used as a part of theenvironmental data as necessary. Thus, even in a case where data cannotbe obtained for a certain period of time due to a failure of a sensor ora communication error, data can be appropriately supplemented and usedfor processing. In addition, the interpolation and prediction of dataare not limited to the above-described techniques, and varioustechniques can be used.

In the above-described embodiment, a GUI for instruction input isprovided by the GUI management part 21, but these are not essentialconfigurations. Policy data may be acquired in any other format. Forexample, a data set indicating an initial setting may be stored in thedata memory 30 in advance, and this data set may be read as the policydata. Alternatively, a character user interface (CUI) may be employed sothat a user inputs an instruction by keyboard input.

In addition, as described above, training by the environmentreproduction part 27 or the exploration part 28 may be started at anytiming, and a user, etc. may be allowed to change the timing as neededaccording to a situation or a control target.

In the above-described embodiment, the exploration part 28 has beendescribed as exploring for an action for a given environmental state totransition to an optimal next state, but is not necessarily limitedthereto. For example, the exploration result by the exploration part 28does not necessarily require only an optimum action to be output, andmay include a sub-optimum action or an action that may be temporarily orunilaterally evaluated as not being optimum. The exploration part 28 canoutput various actions for various environmental states when performingtraining or evaluation. In addition, the exploration part 28 can adoptvarious techniques known as optimum condition exploration or optimumexploration upon training or evaluation.

Further, the type of the actually measured value, the content of thepolicy data, etc. can be variously modified without departing from thescope of the present invention.

In short, the present invention is not limited to the above-describedembodiments as they are, and can be embodied by modifying theconstituent elements without departing from the scope of the inventionat the implementation stage. Further, various inventions can be formedby appropriately combining a plurality of constituent elements disclosedin the above-described embodiments. For example, some constituentelements may be deleted from all the constituent elements indicated inthe embodiments. Furthermore, the constituent elements of the differentembodiments may be appropriately combined.

(Notes)

Some or all of the above-described embodiments can be described asindicated in the following supplementary notes in addition to theclaims, but are not limited thereto.

[C1]

An action optimization device for optimizing an action for controllingan environment in a target space, the action optimization devicecomprising:

-   -   an environmental data acquisition part that acquires        environmental data related to a state of the environment in the        target space;    -   an environmental data interpolation part that performs        time/space interpolation on the acquired environmental data        according to a preset algorithm;    -   an environment reproduction model training part that trains an        environment reproduction model, based on the        time/space-interpolated environmental data, such that, when a        state of an environment and an action for controlling the        environment are input, a correct answer value of an        environmental state after the action is output;    -   an exploration model training part that trains an exploration        model such that an action to be taken next is output when an        environmental state output from the environment reproduction        model is input;    -   an environment reproduction part that predicts a second        environmental state corresponding to a first environmental state        and a first action by using the environment reproduction model;    -   an exploration part that explores for a second action to be        taken for the second environmental state by using the        exploration model; and    -   an output part that outputs a result of the exploration by the        exploration part.        [C2]

The action optimization device according to the above C1, wherein theexploration part outputs the explored for second action to theenvironment reproduction part,

-   -   the environment reproduction part further predicts a third        environmental state corresponding to the second environmental        state and the second action output from the exploration part by        using the environment reproduction model, and    -   the exploration part further explores for a third action to be        taken for the third environmental state by using the exploration        model.        [C3]

The action optimization device according to the above C1, wherein theenvironment reproduction part further outputs a reward corresponding tothe second environmental state based on a preset reward function, and

-   -   the exploration model training part updates a training result of        the exploration model based on the reward output from the        environment reproduction part.        [C4]

The action optimization device according to the above C1, furthercomprising an environment prediction part that performs futureprediction using a preset time-series analysis method based on theenvironmental data to generate environment prediction data,

-   -   wherein the exploration part explores for an action to be taken        by using the environment prediction data for the exploration        model.        [C5]

The action optimization device according to the above C1, furthercomprising an environment expansion part that performs data augmentationon the environmental data based on a random number,

-   -   wherein the environment reproduction model training part trains        the environment reproduction model by using the environmental        data subjected to the data augmentation.        [C6]

The action optimization device according to the above C1, furthercomprising a policy data acquisition part that acquires policy dataspecifying information to be used for processing by the environmentreproduction model training part, the exploration model training part,the environment reproduction part, or the exploration part.

[C7]

The action optimization device according to the above C1, wherein theexploration part explores for, as the second action, an action of agroup unit for a control target group obtained by grouping a pluralityof control targets based on a predetermined criterion in advance, or aseries of actions for one or more control targets for realizing apredetermined function.

[C8]

An action optimization method executed by an action optimization devicefor optimizing an action for controlling an environment in a targetspace, the action optimization method comprising:

-   -   acquiring environmental data related to an environmental state        in the target space;    -   performing time/space interpolation on the acquired        environmental data according to a preset algorithm;    -   training an environment reproduction model, based on the        time/space-interpolated environmental data, such that, when a        state of an environment and an action for controlling the        environment are input, a correct answer value of an        environmental state after the action is output;    -   training an exploration model such that an action to be taken        next is output when an environmental state output from the        environment reproduction model is input;    -   predicting a second environmental state corresponding to a first        environmental state and a first action by using the environment        reproduction model;    -   exploring for a second action to be taken for the second        environmental state by using the exploration model; and    -   outputting a result of the exploration.        [C9]

A program for causing a processor to execute processing by each part ofthe device according to any one of the above C1 to C7.

REFERENCE SIGNS LIST

-   -   1: action optimization device    -   10: input/output interface part    -   20: controller/processor part    -   20A: hardware processor    -   20B: program memory    -   21: GUI management part    -   22: policy management part    -   23: environmental data acquisition part    -   24: environmental data interpolation part    -   25: environment prediction part    -   26: environment expansion part    -   27: environment reproduction part    -   28: exploration part    -   29: transmission control part    -   30: data memory    -   31: environmental data memory    -   32: policy data memory    -   33: model data memory    -   34: exploration result memory    -   210: policy input screen    -   220: exploration instruction input screen    -   250: policy input screen

The invention claimed is:
 1. An action optimization device foroptimizing an action for controlling air conditioning in a target space,comprising a processor and a memory connected to the processor, theaction optimization device comprising: an environmental data acquisitionunit configured to acquire environmental data related to a state of theenvironment in the target space including a people flow, a temperature,and a humidity and to store the acquired a plurality of environmentaldata items in an environmental data storage unit, wherein each of dataof the people flow, the temperature, and the humidity included in theplurality of environmental data items includes a plurality of data itemsacquired at any different time; an environmental data interpolation unitconfigured to perform time/space interpolation on the plurality ofacquired environmental data items according to a preset algorithm; anenvironment reproduction model training unit configured to train anenvironment reproduction model, based on the time/space-interpolatedenvironmental data, such that, when a state of an environment and anaction for controlling the environment are input, a correct answer valueof an environmental state after the action is output; an environmentexpansion unit configured to perform data augmentation on theenvironmental data based on a random number within a range that does notdestroy a relationship between the state of the environment, the action,and the environmental state; wherein the environment reproduction modeltraining unit is configured to train the environment reproduction modelby using the environmental data subjected to the data augmentation; anexploration model training unit configured to train an exploration modelsuch that an action to be taken next is output when an environmentalstate output from the environment reproduction model is input, and storethe trained exploration model in the memory; an action exploration unitconfigured to explore for a first action to be taken for a firstenvironmental state by using the trained exploration model; anenvironment reproduction unit configured to predict a second environmentstate corresponding to the first environment state and the first actionby using the trained environment reproduction model; and an output unitconfigured to output a result of the exploration.
 2. The actionoptimization device according to claim 1, wherein the action explorationunit is configured to further explore for a second action to be takenfor the second environmental state by using the trained explorationmodel; and the environment reproduction unit is configured to furtherpredict a third environment state corresponding to the secondenvironment state and the second action using the trained environmentreproduction model.
 3. The action optimization device according to claim1, further comprising: an environment prediction unit configured toperform future prediction by using a preset time-series analysis methodbased on the environmental data to generate environment prediction dataand store the generated environment prediction data in the environmentdata storage unit as environment data.
 4. The action optimization deviceaccording to claim 1, a policy data acquisition unit configured toacquire policy data specifying information to be used for at least oneprocessing of training the environment reproduction model, training theexploration model, predicting the second environmental state, andexploring for the first action.
 5. The action optimization deviceaccording to claim 1, wherein the action exploration unit is configuredto: explore for, as the first action, an action of a group unit for acontrol target group obtained by grouping a plurality of control targetsbased on a predetermined criterion in advance, or a series of actionsfor one or more control targets for realizing a predetermined function.6. A non-transitory tangible computer-readable storage medium havingstored thereon a program comprising instructions for causing a processorto execute so as to cause a computer to function as the actionoptimization device of any one of claims 1, 2, 3 and
 4. 7. An actionoptimization method for an action optimization device including aprocessor and a memory connected to the processor to optimize an actionfor controlling an environment in a target space, the actionoptimization method comprising: acquiring a plurality of environmentaldata items related to a state of the environment in the target spaceincluding a people flow, a temperature, and a humidity, wherein each ofdata of the people flow, the temperature, and the humidity included inthe plurality of environmental data items includes a plurality of dataacquired at any different time; storing the plurality of acquiredenvironmental data items in an environmental data storage unit;performing time/space interpolation on the acquired environmental dataaccording to a preset algorithm; training an environment reproductionmodel, based on the time/space interpolated environmental data, suchthat, when a state of an environment and an action for controlling theenvironment are input, a correct answer value of an environmental stateafter the action is output, and storing the trained environmentreproduction model in the memory; performing data augmentation on theenvironmental data based on a random number within a range that does notdestroy a relationship between the state of the environment, the action,and the environmental state; wherein the training comprising a trainingthe environment reproduction model by using the environmental datasubjected to the data augmentation; training an exploration model suchthat an action to be taken next is output when an environmental stateoutput from the environment reproduction model is input; exploring for afirst action to be taken for a first environmental state by using thetrained exploration model read; predicting a second environment statecorresponding to the first environment state and the first action byusing the trained environment reproduction model; and outputting aresult of the exploration.